Co-authored by Michael Housman, Chief Data Scientist and Meredith Lackey, Creative Director at RapportBoost.AI.
Leveraging Big Data promises big things, from cost-savings to revenue boosts and organizational efficiency. But the road to get there is shrouded in mystery. Academia is producing professionals that can do the heavy lifting, but the enterprise is just beginning to promote data-driven culture.
Rather than to position Big Data as the next frontier, I’d like to make another claim. Your company is already a Big Data company.
Surprised? Let me explain. Your company is already storing troves of usable data in unusual places, and this data is probably valuable. The MIT Technology Review cites that just .5% of the world’s data is actually analyzed. Furthermore, 71% of companies are unsure how to use their unstructured data, and 75% of companies have never even employed a data analyst. All of this goes to show that companies have a significant amount of the data in their possession to drive profits, lower costs, and deploy marketing dollars more efficiently.
In the conversational commerce arena, the most crucial data is unstructured, comprising text-based customer communications in the form of emails, messages, and social media interactions. Leveraging unstructured data is a big concern for C-suite executives and marketing teams. Data Science allows CMOs to target customers and boost campaigns. CEOs work with Data Scientists to develop insights that facilitate smarter decision making. Some organizations even have in-house Chief Data Officers who leverage unstructured data to optimize company operations.
So are you missing out on value opportunities for your company through disorganized, unstructured data? Most companies are. Before we delve into specifics, let’s parse the difference between structured and unstructured data.
Structured data is easy to query analyze because it exists in columns and rows. This relational organization makes it easy to retrieve, as each piece of information is stored in a specific location. Most of the data that e-commerce companies collect and mine is structured. This includes data from order tracking software, Customer Relationship Management software like Salesforce and Oracle, and SEO markup added to websites to facilitate indexing.
Unstructured data —such as the ad hoc conversations between live chat agents and website visitors— is messy, unorganized, and complex. It exists in many forms, including text conversations, emails, reports, audio and video files, and social media. Unlike structured data, unstructured data doesn’t live in columns and rows. It has to be tagged and analyzed to be useful. If your company uses Customer Relationship Management software, you may think you’ve got Big Data covered. But as of yet, most CRM software doesn’t store and track unstructured data, leaving a treasure trove of valuable insights untagged and unusable.
For an e-commerce company, unstructured data is the key to unlocking huge opportunities for customer engagement and increasing online sales. McKinsey Global Institute estimates that AI technologies that utilize unstructured data have the potential to increase online sales by 30% using dynamic pricing and personalization. But this analysis is often difficult, time-consuming, and lacking precision. Data Scientists must first identify and organize data, apply AI algorithms to data to generate insights, then make those insights actionable to align with business strategy and goals.
Here are three takeaways to guide your company’s unstructured data strategy:
#1 Outsource everything you can to organizations that leverage machine learning. There are hundreds of APIs, or Application Programming Interfaces, that have been built to engage in natural language processing (e.g. entity extraction, parts of speech tagging, sentiment analysis, etc.). They’ve been built over the course of many years and many of them are outstanding. Don’t try to build everything on your own – you can see farther by standing on the shoulders of giants.
#2 Decide if your company is looking for prediction or prescription.If your company is most focused on predicting a particular outcome with more precision, you can easily leverage a number of approaches that encode unique features and build models that are more predictive than any human can build. However, you won’t be able to identify the causality driving the outputs. Black boxes are only so useful. If you want to understand the why, you’ll need to build a considerable amount of this machine learning framework from scratch so that it’s tailored to your specific data.
#3 Use the tools, techniques, and packages that align with your company’s objectives. Unstructured data is unique in that it’s often stored in databases specifically designed to accommodate its messy nature. As a result, you’ll have to give special consideration to how your company’s systems are designed and how they’ll interface with the specialized tools used to analyze them. Efficiency, reliability, and visibility are paramount for determining this system integration.
For example, rather than measuring customer satisfaction with voluntary surveys, companies can use enterprise AI solutions to analyze an entire corpus of unstructured data, figure out the specific sales behaviors that make customers happy, and determine exactly how much these behaviors influence sales outcomes. Additionally, companies can leverage unstructured data to determine how live chat agent behavior relates to specific key performance indicators, including conversions, order size, customer retention, and lifetime customer value.
To see the biggest gains from online sales, e-commerce companies need to organize and analyze their data, regardless of source or type. At RapportBoost.AI, we’ve developed a system that does the heavy lifting so you don’t have to. Our Data Scientists identify, organize, and analyze your unstructured data using AI algorithms, delivering you actionable insights that deliver.
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